Modeling and Reasoning with Bayesian Networks
- 10h 23m
- Adnan Darwiche
- Cambridge University Press
- 2009
This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.
About the Author
Adnan Darwiche is a Professor and Chairman of the Computer Science Department at UCLA. He is also the Editor-in-Chief for the Journal of Artificial Intelligence Research (JAIR) and a AAAI Fellow.
In this Book
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Modeling and Reasoning with Bayesian Networks
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Preface
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Introduction
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Propositional Logic
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Probability Calculus
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Bayesian Networks
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Building Bayesian Networks
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Inference by Variable Elimination
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Inference by Factor Elimination
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Inference by Conditioning
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Models for Graph Decomposition
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Most Likely Instantiations
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The Complexity of Probabilistic Inference
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Compiling Bayesian Networks
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Inference with Local Structure
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Approximate Inference by Belief Propagation
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Approximate Inference by Stochastic Sampling
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Sensitivity Analysis
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Learning: The Maximum Likelihood Approach
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Learning: The Bayesian Approach
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Bibliography
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